Contrast Modification Forensics Algorithm Based on Merged Weight Histogram of Run Length

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Author(s): Liang Yang (Nankai University, Tianjin, China), Tiegang Gao (College of Software, Nankai University, Tianjin, China), Yan Xuan (Nankai University, Tianjin, China)and Hang Gao (Nankai University, Tianjin, China)
Copyright: 2020
Pages: 11
Source title: Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2466-4.ch016

Keywords: Cyber Warfare & Terrorism / Cyber Warfare and Cyber Terrorism / Information Science Reference / Security & Forensics

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Abstract

A novel image forensic algorithm against contrast modification based on merged weight histogram of run length is proposed. In the proposed algorithm, the run length histogram features were firstly extracted, and then those of different orientation were subsequently merged; after normalization of the prior features, the authors calculated leaps in the histogram numerically; lastly, the generated features of authentic and tampered images were trained by a SVM classifier. Large amounts of experiments show that, the proposed algorithm has low cost of computation complexity, compared with some existing scheme, and it has better performance with many test databases, furthermore, the proposed algorithm can effectively detect local contrast modification of image.

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Contrast Modification Forensics Algorithm Based on Merged Weight Histogram of Run Length

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